Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study

• Published in: Abdominal imaging Vol. 46; no. 10; pp. 4576 - 4587
• Main Authors: Wang, Shu-Hui, Du, Jing, Xu, Hui, Yang, Dawei, Ye, Yuxiang, Chen, Yinan, Zhu, Yajing, Ba, Te, Yuan, Chunwang, Yang, Zheng-Han
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2021; Springer Nature B.V
• Subjects: Accuracy; Artificial intelligence; Cirrhosis; Gastroenterology; Hepatobiliary; Hepatology; Imaging; Liver; Liver cirrhosis; Magnetic resonance imaging; Medicine; Medicine & Public Health; Neural networks; Performance evaluation; Phases; Radiology; Recognition; Sensitivity; Sensitivity analysis; Test sets; Magnetic resonance imaging (MRI); Artificial intelligence; Liver
• ISSN: 2366-004X; 2366-0058
• DOI: 10.1007/s00261-021-03142-4

Purpose To develop and validate a dense feature fusion neural network (DFuNN) to automatically recognize different sequences and phases of liver magnetic resonance imaging (MRI). Materials and methods In total, 3869 sequences and phases from 384 liver MRI examinations, divided into training/validation ( n  = 2886 sequences from 287 patients) and test ( n  = 983 sequences from 97 patients) sets, were used in this retrospective study. Ten unenhanced sequences and enhanced phases were included. Manual sequence recognition, performed by two radiologists (20 and 10 years of experience) in a consensus reading, was used as the reference standard. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the DFuNN on an identical unseen test set. Finally, we evaluated the factors impacting the model precision. Results A fusion block improved the performance of the DFuNN. DFuNN with a fusion block achieved good recognition performance for both complete and incomplete sequences and phases in the test set. The average sensitivity of recognition performance for complete sequence and phase inputs ranged from 88.06 to 100%, the average specificity ranged from 99.12 to 99.94%, and the median accuracy ranged from 98.02 to 99.95%. The DFuNN prediction accuracy for patients without cirrhosis were significantly higher than those for patients with cirrhosis ( P  = 0.0153). No significant difference was found in the accuracy across other factors. Conclusion DFuNN can automatically and accurately identify specific unenhanced MRI sequences and enhanced MRI phases.